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Energies 2016, 9(3), 186; doi:10.3390/en9030186

Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making

Department of Electrical and Information Engineering, Hunan University, Changsha 410082, China
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Neville R. Watson
Received: 18 November 2015 / Revised: 22 February 2016 / Accepted: 3 March 2016 / Published: 11 March 2016
(This article belongs to the Special Issue Microgrids 2016)
View Full-Text   |   Download PDF [662 KB, uploaded 11 March 2016]   |  

Abstract

This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO) algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO2 emissions, wind curtailment, and EV users’ cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS) thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy. View Full-Text
Keywords: electric vehicle (EV); coordinated charging; optimal scheduling; vehicle-to-grid (V2G); smart grid electric vehicle (EV); coordinated charging; optimal scheduling; vehicle-to-grid (V2G); smart grid
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Liu, D.; Wang, Y.; Shen, Y. Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making. Energies 2016, 9, 186.

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